A common defective phenomenon in rotating machinery is rotor-casing rub that generates impacts when the rotor rubs against the stator. Vibration sensors and data analysis techniques are commonly used for fault signature extraction and mechanical systems diagnosis. In this paper, an experimental characterization of rotor-rub is made by time-frequency analysis by means of the wavelet transform. A rotor kit, equipped with a variable speed DC motor, an accelerometer and a data acquisition system are used to acquire the mechanical vibration data. Vibration signal in frequency and time-frequency domains are shown for no-rubbing, light, and severe rubbing cases. Results show that FFT is unable to report where in time particular components of rubbing appear. However, the time-frequency analysis is able to give location information in time to differentiate light from severe rubbing, and extract the main spectral components showing a spectrum rich in high frequency components, characteristic of this phenomenon.
Rubbing is an important problem in machinery industry which occurs when a rotating element hits a stationary part. This rotor-to-stator rub may result in the catastrophic breakdown of the machine. In this work, the phenomenon of rotor rubbing is analyzed from the perspective that the signal analysis tools that are in use today to detect this defect emphasize or highlight particular aspects of the studied phenomenon. So, sometimes it is necessary to use more than one tool to deepen the understanding of the problem. For this purpose, laboratory tests were performed on a rotor system with a rubbing mechanism, while mechanical vibrations were measured with an accelerometer and a data acquisition system. Experiments were carried out for fixed rotor speed, and for run-up and run-down rotor speed conditions. The analysis approach included various processing tools to study their capabilities in rubbing detection: Root Mean Square (RMS), Fourier transform, Wavelet transform and Hurst exponent. Fixed rubbing conditions show similar results for RMS and Hurst exponent on the information obtained. For variable run-up and run-down rotor speed conditions, the Hurst exponent shows predictability, a fact that can be used for rub detection. However, the Wavelet and Fourier Transforms operated in a very distinct way. Although both transforms give frequency information, Fourier transform results in a more detailed frequency analysis, while the Wavelet transform can give time localization of the rubbing phenomenon.
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